AI/machine learning
Speakers at ATCE said innovative technologies will play a critical role in balancing energy security, affordability, and sustainability in the years ahead.
Known as Arena, the collaboration aims to improve decision-making across increasingly complex reservoirs and growing volumes of subsurface data.
Robotic and autonomous systems are transforming energy operations, but their full value emerges only when paired with data science, engineering analytics, and AI/ML to turn data into decisions. The transition of the SPE Robotic & Autonomous Systems Technical Section into the SPE Data Science & Engineering Analytics Technical Section helps bridge this critical technolo…
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The algorithms for running AI applications have been so big that they’ve required powerful machines in the cloud and data centers, making many applications less useful on smartphones and other edge devices. Now, that concern is quickly melting away, thanks to a series of recent breakthroughs. -
This paper describes a path to general artificial intelligence (AI) (i.e., AI that is as smart or smarter than humans) based on the trend in machine learning that hand-designed solutions eventually are replaced by more-effective, learned solutions.
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Arundo Analytics has built an integrated industrial Internet of things platform that allows data scientists to productize data-science solutions and accelerate feedback/improvement iterations between end-users and data scientists effectively.
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Microsoft announced three new services that aim to simplify the process of machine learning—an interface for a tool that automates the process of creating models; a new no-code visual interface for building, training, and deploying models; and hosted Jupyter-style notebooks for advanced users.
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Analytics, sensors, and robots are changing the way one of the world’s largest oil and gas companies does business. Underpinning all the new technology though is a shift in how BP thinks, and what it means to be a supermajor in the 21st century.
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Maybe we don’t need to look inside the black box after all. Maybe we just need to watch how machines behave, instead.
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The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.
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This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.
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Machine learning (ML) finds patterns in data. "AI bias" means that it might find the wrong patterns. Meanwhile, the mechanics of ML might make this hard to spot.
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Rapid advances in deep learning continue to demonstrate the significance of end-to-end training with no a priori knowledge. However, when models need to do forward prediction, most AI researchers agree that incorporating prior knowledge with end-to-end training can introduce better inductive bias.
